Concomitant medication, comorbidity and survival in patients with breast cancer.

Between 30% and 70% of patients with breast cancer have pre-existing chronic conditions, and more than half are on long-term non-cancer medication at the time of diagnosis. Preliminary epidemiological evidence suggests that some non-cancer medications may affect breast cancer risk, recurrence, and survival. In this nationwide cohort study, we assessed the association between medication use at breast cancer diagnosis and survival. We included 235,368 French women with newly diagnosed non-metastatic breast cancer. In analyzes of 288 medications, we identified eight medications positively associated with either overall survival or disease-free survival: rabeprazole, alverine, atenolol, simvastatin, rosuvastatin, estriol (vaginal or transmucosal), nomegestrol, and hypromellose; and eight medications negatively associated with overall survival or disease-free survival: ferrous fumarate, prednisolone, carbimazole, pristinamycin, oxazepam, alprazolam, hydroxyzine, and mianserin. Full results are available online from an interactive platform ( https://adrenaline.curie.fr ). This resource provides hypotheses for drugs that may naturally influence breast cancer evolution.

Evolution of synchronous female bilateral breast cancers and response to treatment.

Synchronous bilateral breast cancer (sBBC) occurs after both breasts have been affected by the same germline genetics and environmental exposures. Little evidence exists regarding immune infiltration and response to treatment in sBBCs. Here we show that the impact of the subtype of breast cancer on levels of tumor infiltrating lymphocytes (TILs, n = 277) and on pathologic complete response (pCR) rates (n = 140) differed according to the concordant or discordant subtype of breast cancer of the contralateral tumor: luminal breast tumors with a discordant contralateral tumor had higher TIL levels and higher pCR rates than those with a concordant contralateral tumor. Tumor sequencing revealed that left and right tumors (n = 20) were independent regarding somatic mutations, copy number alterations and clonal phylogeny, whereas primary tumor and residual disease were closely related both from the somatic mutation and from the transcriptomic point of view. Our study indicates that tumor-intrinsic characteristics may have a role in the association of tumor immunity and pCR and demonstrates that the characteristics of the contralateral tumor are also associated with immune infiltration and response to treatment.

CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data.

Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.

No Impact of Smoking Status on Breast Cancer Tumor Infiltrating Lymphocytes, Response to Neoadjuvant Chemotherapy and Prognosis.

Tobacco use is associated with an increase in breast cancer (BC) mortality. Pathologic complete response (pCR) rate to neoadjuvant chemotherapy (NAC) is influenced by tumor-infiltrating lymphocyte (TIL) levels and is associated with a better long-term survival outcome. The aim of our study is to evaluate the impact of smoking status on TIL levels, response to NAC and prognosis for BC patients. We retrospectively evaluated pre- and post-NAC stromal and intra tumoral TIL levels and pCR rates on a cohort of T1-T3NxM0 BC patients treated with NAC between 2002 and 2012 at Institut Curie. Smoking status (current, ever, never smokers) was collected in clinical records. We analyzed the association between smoking status, TIL levels, pCR rates and survival outcomes among the whole population, and according to BC subtype. Nine hundred and fifty-six BC patients with available smoking status information were included in our analysis (current smokers, n = 179 (18.7%); ever smokers, n = 154 (16.1%) and never smokers, n = 623 (65.2%)). Median pre-NAC TIL levels, pCR rates, or median post-NAC TIL levels were not significantly different according to smoking status, neither in the whole population, nor in any BC subtype group. With a median follow-up of 101.4 months, relapse-free survival (RFS) and overall survival (OS) were not significantly different by smoking status. We did not find any significant effect of tobacco use on pre- and post-NAC TILs nor response to NAC. Though our data seem reassuring, BC treatment should still be considered as a window of opportunity to offer BC patients accurate smoking cessation interventions.

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data.

Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications. In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables. In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

Author Correction: New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels.

One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. The so-called "drug repositioning process" propose to find new therapeutic indications to already approved drugs. For this, new analytic methods are required to optimize the information present in large-scale pharmacogenomics datasets. We analyzed data from the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia studies. We focused on common cell lines (n = 471), considering the molecular information, and the drug sensitivity for common drugs screened (n = 15). We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our robust molecular classification displays greater homogeneity of drug sensitivity than cancer cell line grouped based on tissue of origin. We then identified significant associations between cell line cluster and drug response robustly found between both datasets. We further demonstrate the relevance of our method using two additional external datasets and distinct sensitivity metrics. Some associations were still found robust, despite cell lines and drug responses' variations. This study defines a robust molecular classification of cancer cell lines that could be used to find new therapeutic indications to known compounds.

A Stromal Immune Module Correlated with the Response to Neoadjuvant Chemotherapy, Prognosis and Lymphocyte Infiltration in HER2-Positive Breast Carcinoma Is Inversely Correlated with Hormonal Pathways.

INTRODUCTION: HER2-positive breast cancer (BC) is a heterogeneous group of aggressive breast cancers, the prognosis of which has greatly improved since the introduction of treatments targeting HER2. However, these tumors may display intrinsic or acquired resistance to treatment, and classifiers of HER2-positive tumors are required to improve the prediction of prognosis and to develop novel therapeutic interventions. METHODS: We analyzed 2893 primary human breast cancer samples from 21 publicly available datasets and developed a six-metagene signature on a training set of 448 HER2-positive BC. We then used external public datasets to assess the ability of these metagenes to predict the response to chemotherapy (Ignatiadis dataset), and prognosis (METABRIC dataset). RESULTS: We identified a six-metagene signature (138 genes) containing metagenes enriched in different gene ontologies. The gene clusters were named as follows: Immunity, Tumor suppressors/proliferation, Interferon, Signal transduction, Hormone/survival and Matrix clusters. In all datasets, the Immunity metagene was less strongly expressed in ER-positive than in ER-negative tumors, and was inversely correlated with the Hormonal/survival metagene. Within the signature, multivariate analyses showed that strong expression of the "Immunity" metagene was associated with higher pCR rates after NAC (OR = 3.71[1.28-11.91], p = 0.019) than weak expression, and with a better prognosis in HER2-positive/ER-negative breast cancers (HR = 0.58 [0.36-0.94], p = 0.026). Immunity metagene expression was associated with the presence of tumor-infiltrating lymphocytes (TILs). CONCLUSION: The identification of a predictive and prognostic immune module in HER2-positive BC confirms the need for clinical testing for immune checkpoint modulators and vaccines for this specific subtype. The inverse correlation between Immunity and hormone pathways opens research perspectives and deserves further investigation.

The 3D organization of chromatin explains evolutionary fragile genomic regions.

Genomic rearrangements are a major source of evolutionary divergence in eukaryotic genomes, a cause of genetic diseases and a hallmark of tumor cell progression, yet the mechanisms underlying their occurrence and evolutionary fixation are poorly understood. Statistical associations between breakpoints and specific genomic features suggest that genomes may contain elusive "fragile regions" with a higher propensity for breakage. Here, we use ancestral genome reconstructions to demonstrate a near-perfect correlation between gene density and evolutionary rearrangement breakpoints. Simulations based on functional features in the human genome show that this pattern is best explained as the outcome of DNA breaks that occur in open chromatin regions coming into 3D contact in the nucleus. Our model explains how rearrangements reorganize the order of genes in an evolutionary neutral fashion and provides a basis for understanding the susceptibility of "fragile regions" to breakage.